Leobard's blog

Gunnar Grimnes pointed me to this article by Roger
C. Schank

Note the best part here on Five issues to think about before you try to do real AI, which is related to my other rants about researchers who should dig into engineering:

1. Real problems are needed for prototyping. We cannot keep working in toy domains. Real problems identify real users with real needs. This changes what the interactions with the program will be considerably and must be part of the original design.

2. Real knowledge that real domain experts have must be found and stored. This does not mean interviewing them and asking for the rules that they use and ignoring everything else that fails to fit. Real experts have real experiences, contradictory viewpoints, exceptions, confusions, and the ability to have an intuitive feel for a problem. Getting at these issues is critical. It is possible to build interesting systems that do not know what they know. Expertise can be captured in video, stored and indexed in a sound way, and retrieved without having to fully represent the content of that expertise (e.g., the ASK TOM system (Schank, Ferguson, Birnbaum, Barger, Greising, 1991). Such a system would be full of AI ideas, interesting to interact with, and not wholly intelligent but a far sight better than systems that did not have such knowledge available.

3. Software engineering is harder than you think. I can’t emphasize strongly enough how true this is. AI had better deal with the problem.

4. Everyone wants to do research. One serious problem in AI these days is that we keep producing researchers instead of builders. Every new Ph.D. receipient, it seems, wants to continue to work on some obscure small problem whose solution will benefit some mythical program that no one will ever write. We are in danger of creating a generation of computationally sophisticated philosophers. They will have all the usefulness and employability of philosophers as well.

5. All that matters is tool building. This may seem like an odd statement considering my comments about the expert system shell game. However, ultimately we will not be able to build each new AI system from scratch. When we start to build useful systems the second one should be easier to build than the first, and we should be able to train non-AI experts to build them. This doesn’t mean that these tools will allow everyone to do AI on their personal computers. It does mean that certain standard architectures should evolve for capturing and finding knowledge. From that point of view the shell game people were right, they just put the wrong stuff in the shell. The shell should have had expert knowledge about various domains in it, available to make the next system in that domain that much easier to build.

Aduna, the company that is known for guided exploration of compnay information, and of course for programming the sesame server at openrdf.org, and for autofocus, and and and …. they have swichted their business model to open source.

read their statement on open-source.

They have also changed their name to “Aduna-Software”, giving focus on what they do (software).

This continues what I have experienced working with them together on Aperture, the framework I have been using now in the latest gnowsis system to crawl data from various file-formats and data sources. We developed this together, they use it for the new version of Autofocus.

Aperture raised interest, Henry Story from SUN blogged about how important it is to have projects like Aperture to make the semantic web run.

A new website was created to separate open-source services from company issues, this seems to be their open-source portal: aduna-software.org

and look, they use their own tech to crawl their own website and have a search interface.

So, best wishes for Aduna-Software from my side and looking forward for the release of Sesame2.

Zack Rosen blogs about why RDF research sucks and has written a mail to the Simile mailinglist for comments. No comments from me, but a general agreement on his suggestions for a way out:

So what can we do about it?

1. Researchers need to stop thinking of themselves as researchers and start thinking of themselves as implementors.
2. Research institutes need to join forces with emerging businesses looking to adopt semantic technology. This breaks the current model of business / research institute collaboration since startups do not have money to contribute to fund research, but tough noogies.
3. Researchers need to build their tools in real-world development environments, i.e. as modules for LAMP web-publishing tools such as Drupal and WordPress. They need to find more organizational partners to deploy their solutions. They need to do something other than build widgets.